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ISSR-Based Genetic Diversity Assessment of Genus Jasminum L. (Oleaceae) from Pakistan.

Naeem AkhtarIshfaq Ahmad HafizMuhammad Qasim HayatDaniel PotterNadeem Akhtar AbbasiUmer HabibAdil HussainHina HafeezMuhammad Ajmal BashirSaad Imran Malik
Published in: Plants (Basel, Switzerland) (2021)
The genus Jasminum L., of the family Oleaceae, includes many species occurring in the wild, or cultivated worldwide. A preliminary investigation based on inter-simple sequence repeats (ISSR) was performed to assess the genetic diversity among 28 accessions, representing nine species of Jasminum from various regions, representing a range of altitudes in Pakistan. A total of 21 ISSR primers were used, which produced 570 amplified bands of different sizes, with a mean polymorphic band percentage of 98.26%. The maximum resolving power, polymorphism information content, and index values of the ISSR markers recorded for primers 6, 16, and 19 were 0.40, 12.32, and 24.21, respectively. Based on the data of the ISSR markers, the resulting UPGMA dendrogram with the Jaccard coefficient divided the 28 accessions into two main clades. At the species level, the highest values for Shannon's information index, polymorphism percentage, effective allele number, Nei's genetic variations, and genetic unbiased diversity were found in Jasminum sambac L. and J. humile L., while the lowest were observed in J. mesnyi Hance and J. nitidum Skan. Based on Nei's unbiased genetic identity pairwise population matrix, the maximum identity (0.804) was observed between J. elongatum Willd and J. multiflorum (Burm. f.) Andrews, and the lowest (0.566) between J. nitidum Skan. and J. azoricum L. Molecular variance analysis displayed a genetic variation of 79% among the nine populations. The study was aimed to established genetic diversity in Jasminum species using ISSR markers. With the help of this technique, we were able to establish immense intra- and interspecific diversity across the Jasminum species.
Keyphrases
  • genetic diversity
  • genome wide
  • magnetic resonance imaging
  • electronic health record
  • machine learning
  • magnetic resonance
  • tertiary care
  • deep learning